Improving Accuracy for Diabetes Mellitus Prediction by Using Deepnet

Alshammari, Riyad; Atiyah, Noorah; Daghistani, Tahani; Alshammari, Abdulwahhab

doi:10.5210/ojphi.v12i1.10611

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…For T2D risk prediction models, logistic regression has been frequently used and shown to be effective in supervised classification (Uddin et al, 2019; Xie et al, 2019). A recent study that compared machine learning models for the identification of patients with T2D reported logistic regression having the highest accuracy of 88.19% while decision trees had the lowest accuracy of 87.8% (Alshammari et al, 2020). Although studies that used extra trees for T2D risk prediction were not identified, the model is similar to random forest but contains random splits of observations which leads to lower levels of variance (Geurts et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Prediction Performance of Feature Selectors and Classifiers on Highly Dimensional Transcriptomic Data for Prediction of Weight Loss in Filipino Americans at Risk for Type 2 Diabetes

Chang

Fukuoka

Aouizerat

et al. 2023

Biological Research For Nursing

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Background: Accurate prediction of risk for chronic diseases like type 2 diabetes (T2D) is challenging due to the complex underlying etiology. Integration of more complex data types from sensors and leveraging technologies for collection of -omics datasets may provide greater insights into the specific risk profile for complex diseases. Methods: We performed a literature review to identify feature selection methods and machine learning models for prediction of weight loss in a previously completed clinical trial (NCT02278939) of a behavioral intervention for weight loss in Filipinos at risk for T2D. Features included demographic and clinical characteristics, dietary factors, physical activity, and transcriptomics. Results: We identified four feature selection methods: Correlation-based Feature Subset Selection (CfsSubsetEval) with BestFirst, Kolmogorov–Smirnov (KS) test with correlation featureselection (CFS), DESeq2, and max-relevance-min-relevance (MRMR) with linear forward search and mutual information (MI) and four machine learning algorithms: support vector machine, decision tree, random forest, and extra trees that are applicable to prediction of weight loss using the specified feature types. Conclusion: More accurate prediction of risk for T2D and other complex conditions may be possible by leveraging complex data types from sensors and -omics datasets. Emerging methods for feature selection and machine learning algorithms make this type of modeling feasible.

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Section: Introductionmentioning

confidence: 99%

Prediction Performance of Feature Selectors and Classifiers on Highly Dimensional Transcriptomic Data for Prediction of Weight Loss in Filipino Americans at Risk for Type 2 Diabetes

Chang

Fukuoka

Aouizerat

et al. 2023

Biological Research For Nursing

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“…Diabetes is associated with premature deaths from both communicable and noncommunicable diseases. Therefore, early prediction of DM has great potential to reduce financial burden as well as increase life expectancy [4]. Machine learning is a growing field in healthcare for its promising performance in term of early prediction, diagnosis, classification, and risk stratification of diseases [5].…”

Section: Introductionmentioning

confidence: 99%

Early Diabetes Prediction: A Comparative Study Using Machine Learning Techniques

Poly

Islam

2022

Studies in Health Technology and Informatics

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Most screening tests for Diabetes Mellitus (DM) in use today were developed using electronically collected data from Electronic Health Record (EHR). However, developing and under-developing countries are still struggling to build EHR in their hospitals. Due to the lack of HER data, early screening tools are not available for those countries. This study develops a prediction model for early DM by direct questionnaires for a tertiary hospital in Bangladesh. Information gain technique was used to reduce irreverent features. Using selected variables, we developed logistic regression, support vector machine, K-nearest neighbor, Naïve Bayes, random forest (RF), and neural network models to predict diabetes at an early stage. RF outperformed other machine learning algorithms achieved 100% accuracy. These findings suggest that a combination of simple questionnaires and a machine learning algorithm can be a powerful tool to identify undiagnosed DM patients.

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“…This reduces the risk of human error when making important healthcare decisions. As a result, the health burden is reduced, and resources are better utilized (Alshammari et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of Diabetes Disease Using Machine Learning Methods

Samet

Laouar

Bendib

et al. 2022

International Journal of Decision Support System Technology

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To increase healthcare quality, early illness prediction helps patients prevent potentially life-threatening health issues before it is too late. Artificial intelligence is a rapidly evolving area, and its applications to diabetes, a worldwide epidemic, have the potential to revolutionize the way diabetes is diagnosed and managed. A total of six supervised machine learning algorithms based on patient data were used and compared to predict the diagnosis of diabetes mellitus. For experiments, the Pima Indians Diabetes Database was used, and their missing values were carefully handled by different techniques. For random train-test splits, the Random Forest classification algorithm achieved an accuracy rate of 92 percent. This model outperforms other state-of-the-art approaches due to the application of a combination of techniques for dealing with missing values (the mixture of imputing missing values techniques) that is proposed. With this approach, the models of this manuscript achieved better accuracy than prior work done with the Pima diabetes data.

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Improving Accuracy for Diabetes Mellitus Prediction by Using Deepnet

Cited by 7 publications

References 28 publications

Prediction Performance of Feature Selectors and Classifiers on Highly Dimensional Transcriptomic Data for Prediction of Weight Loss in Filipino Americans at Risk for Type 2 Diabetes

Prediction Performance of Feature Selectors and Classifiers on Highly Dimensional Transcriptomic Data for Prediction of Weight Loss in Filipino Americans at Risk for Type 2 Diabetes

Early Diabetes Prediction: A Comparative Study Using Machine Learning Techniques

Analysis and Prediction of Diabetes Disease Using Machine Learning Methods

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